+ NV-jn&^RIt^RIHtHt^RIHt^RIHtHtH t H t ^RI H t ^RIHtRtRtRtR tR R ltRR R lltRRRlltRRRllt!RR]4tR#)N)partialreduce)product)CallableLiteralTupleUnion)Modulec\W,4pV'dD\P!V\PR7V^, V^, , ,pMb^V, pV^, V,V, ^,^, p\P!V\PR7V,V, p^.V,p RW&VP V 4#)dtype)intmxarangefloat32reshape) Nscale align_cornersdimndimsMindicesstepstartshapes &&&&& g/Volumes/fast/ai/experiments/ui-tars-smoke/.venv/lib/python3.14/site-packages/mlx/nn/layers/upsample.py_scaled_indicesr s EIA))ARZZ0QUq1u4EF5ya%4!#a'1,))ARZZ047%? C%KEEJ ??5 !!cn\W,4p\P!V\PR7pW@8d/VR,W, ,R, pVP 4pMWPV, ,p^.V,pRWb&VP \P 4PV4#)r g?r)rrrrroundastypeuint32r)rrrrrrrs&&&& r_nearest_indicesr%s EIAii,GuS=QU+c1--/U# C%KEEJ >>")) $ , ,U 33r c\WW#V4p\P!V^V^, R7p\P!V4p\P!V4pWV, p\P !VR4pVP \P4^V, 3VP \P4V33#)ra_mina_maxr)rrclipfloorceil expand_dimsr#r$) rrrrrr indices_l indices_rweights &&&&& r_linear_indicesr1(sa EBGgggQa!e4G!I I  F ^^FB 'F   ")) $a&j1   ")) $f- r ct\WW#V4p\P!V4p\P!V^,4pV^, pV^,p \\PRR7R4p V !WV^R7R,p V !WW^R7R,p V !WX^R7R,p V !WY^R7R,p\P !V^V^, R7p\P !V^V^, R7p\P !V^V^, R7p\P !V ^V^, R7p VP \P4V 3VP \P4V 3VP \P4V 3V P \P4V33#)T) shapelesscRp\P!W, 4pV^8Xd6VR,V,VR,, V,V,^,pV#V^, V,^,V,^, V,pV#)g?g@g@g)rabs)indgriddistaxr0s&&& r _get_weight#_cubic_indices.._get_weight=ss  FF3:  193w!mq3w/14q81_interpolate..s;%:62rww%:s) rPrQrRrr[r\r]rrZroperatormulsum)r;rJrgrr^r`rrarrbrcrdsamplesweights idx_weightidxr0s&&&& r _interpolaterxzs 66A:D s<  RSSwwHA1Gs134 6Az! $?@5GGw' :& Vq%+#-./vhllF34( ;S%:; ;;r cPV^8dQhR\PR\R\/#rIr;rJrrrKrrh)rLs"rrMrMs%rxxuTr c(\VV\VR7#)r;rJrgr)rxr1r;rJrs&&&rupsample_linearrs  !"#  r cPV^8dQhR\PR\R\/#rzr{)rLs"rrMrMs%bhheDr c(\VV\VR7#r})rxrGr~s&&&rupsample_cubicrs  !!#  r cjaa]tRt^toRtR V3RlV3RllltV3RlRltV3RlRltRtVt V;t #) Upsamplea Upsample the input signal spatially. The spatial dimensions are by convention dimensions ``1`` to ``x.ndim - 2``. The first is the batch dimension and the last is the feature dimension. For example, an audio signal would be 3D with 1 spatial dimension, an image 4D with 2 and so on and so forth. There are three upsampling algorithms implemented nearest neighbor upsampling, linear interpolation, and cubic interpolation. All can be applied to any number of spatial dimensions. The linear interpolation will be bilinear, trilinear etc when applied to more than one spatial dimension. And cubic interpolation will be bicubic when there are 2 spatial dimensions. .. note:: When using one of the linear or cubic interpolation modes the ``align_corners`` argument changes how the corners are treated in the input image. If ``align_corners=True`` then the top and left edge of the input and output will be matching as will the bottom right edge. Parameters: scale_factor (float or tuple): The multiplier for the spatial size. If a ``float`` is provided, it is the multiplier for all spatial dimensions. Otherwise, the number of scale factors provided must match the number of spatial dimensions. mode (str, optional): The upsampling algorithm, either ``"nearest"``, ``"linear"`` or ``"cubic"``. Default: ``"nearest"``. align_corners (bool, optional): Changes the way the corners are treated during ``"linear"`` and ``"cubic"`` upsampling. See the note above and the examples below for more details. Default: ``False``. Examples: >>> import mlx.core as mx >>> import mlx.nn as nn >>> x = mx.arange(1, 5).reshape((1, 2, 2, 1)) >>> x array([[[[1], [2]], [[3], [4]]]], dtype=int32) >>> n = nn.Upsample(scale_factor=2, mode='nearest') >>> n(x).squeeze() array([[1, 1, 2, 2], [1, 1, 2, 2], [3, 3, 4, 4], [3, 3, 4, 4]], dtype=int32) >>> b = nn.Upsample(scale_factor=2, mode='linear') >>> b(x).squeeze() array([[1, 1.25, 1.75, 2], [1.5, 1.75, 2.25, 2.5], [2.5, 2.75, 3.25, 3.5], [3, 3.25, 3.75, 4]], dtype=float32) >>> b = nn.Upsample(scale_factor=2, mode='linear', align_corners=True) >>> b(x).squeeze() array([[1, 1.33333, 1.66667, 2], [1.66667, 2, 2.33333, 2.66667], [2.33333, 2.66667, 3, 3.33333], [3, 3.33333, 3.66667, 4]], dtype=float32) cP<V^8dQhRS[S[S[3,RS[R,RS[/#)rIrJmodernearestlinearcubic)r floatrrrh)rL __classdict__s"rrMUpsample.__annotate__s8++E5L)+23+ +r c<\SV`4VR9d\RV 24h\V\\ 34'd \ \ \V44VnM\V4VnW n W0n R#)rz1[Upsample] Got unsupported upsampling algorithm: Nr) super__init__rR isinstancerUrSrTrrJrr)selfrJrr __class__s&&&&rrUpsample.__init__sk  5 5PQUPVWX X lT5M 2 2 %c%&> ?D  %l 3D  *r c <V^8dQhRS[/#)rIreturn)str)rLrs"rrMrs  S r cVRVP RVP: RVP 2#)z scale_factor=z, mode=z, align_corners=)rJrr)rs&r _extra_reprUpsample._extra_reprs6D--.gdii]C!//0 2 r cN<V^8dQhRS[PRS[P/#)rIr;r)rrK)rLrs"rrMrs'HH"((HrxxHr cVP^, pV^8:d\RVP R24hVPp\V\4'd#\ V4V8wd\RV RV 24hM V3V,pVP R8Xd \W4#VP R8Xd\WVP4#VP R8Xd\WVP4#\RVP 24h) rIzg[Upsample] The input should have at least 1 spatial dimension which means it should be at least 3D but zD was providedzH[Upsample] One scale per spatial dimension is required but scale_factor=z+ and the number of spatial dimensions were rrrzUnknown interpolation mode: ) rPrRrJrrSrQrrerrr Exception)rr;r^rJs&& r__call__Upsample.__call__svvz 19F66(.*  (( lE * *< D( $$0>2''+f.))?T1L 99 !#A4 4 YY( ""1D4F4FG G YY' !!!43E3EF F:499+FG Gr )rrrJ)rF) __name__ __module__ __qualname____firstlineno____doc__rrr__static_attributes____classdictcell__ __classcell__)rrs@@rrrs1;z++   HHHr r)F)rq functoolsrr itertoolsrtypingrrrr mlx.corecorermlx.nn.layers.baser rr%r1rGrerxrrrrkr rrsU%22% " 4 #L<<4oHvoHr